Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	118,702
2	South Dakota	107,961
3	Iowa	85,669
4	Wisconsin	85,424
5	Nebraska	81,978
6	Utah	79,565
7	Rhode Island	76,637
8	Tennessee	76,067
9	Idaho	74,395
10	Wyoming	73,223
11	Montana	72,996
12	Illinois	72,070
13	Kansas	71,433
14	Minnesota	71,116
15	Indiana	70,526
16	Arkansas	67,946
17	Nevada	67,318
18	Alabama	67,264
19	Oklahoma	67,127
20	Mississippi	66,425
21	Arizona	64,620
22	Missouri	64,112
23	New Mexico	62,987
24	Louisiana	62,588
25	Alaska	60,476
26	Florida	56,943
27	Texas	56,294
28	Kentucky	55,977
29	Colorado	54,768
30	Georgia	54,623
31	Ohio	54,497
32	South Carolina	54,004
33	Delaware	53,013
34	Michigan	50,464
35	California	49,818
36	New Jersey	49,793
37	Massachusetts	48,048
38	Connecticut	47,390
39	North Carolina	46,838
40	Pennsylvania	45,085
41	New York	44,781
42	Maryland	42,289
43	West Virginia	41,702
44	District of Columbia	38,115
45	Virginia	36,843
46	Puerto Rico	32,416
47	Washington	30,493
48	New Hampshire	27,952
49	Oregon	24,916
50	Maine	14,687
51	Hawaii	14,469
52	Vermont	10,589

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,219
2	Rhode Island	1,061
3	California	1,024
4	Arizona	895
5	Oklahoma	821
6	Indiana	694
7	Idaho	674
8	Alabama	673
9	West Virginia	655
10	Ohio	644
11	Utah	635
12	Mississippi	624
13	Pennsylvania	618
14	Louisiana	611
15	New Hampshire	588
16	Connecticut	577
17	Massachusetts	575
18	Nevada	561
19	North Carolina	557
20	Georgia	548
21	Arkansas	544
22	Texas	539
23	Kentucky	525
24	Wyoming	513
25	Kansas	510
26	Delaware	509
27	New York	500
28	New Mexico	499
29	Missouri	486
30	South Carolina	486
31	Nebraska	485
32	New Jersey	485
33	Florida	463
34	Illinois	454
35	Virginia	449
36	South Dakota	441
37	Montana	408
38	Colorado	407
39	Wisconsin	383
40	Minnesota	376
41	Maryland	371
42	Alaska	352
43	Iowa	352
44	Michigan	290
45	North Dakota	290
46	Puerto Rico	281
47	District of Columbia	263
48	Oregon	258
49	Maine	248
50	Washington	247
51	Vermont	141
52	Hawaii	93

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,063
2	New York	1,865
3	Massachusetts	1,712
4	North Dakota	1,632
5	Connecticut	1,599
6	Rhode Island	1,583
7	South Dakota	1,561
8	Louisiana	1,539
9	Mississippi	1,508
10	Illinois	1,316
11	Michigan	1,234
12	Iowa	1,157
13	Indiana	1,126
14	Arizona	1,117
15	Pennsylvania	1,112
16	Arkansas	1,106
17	District of Columbia	1,054
18	New Mexico	1,050
19	Florida	966
20	South Carolina	966
21	Georgia	954
22	Nevada	919
23	Maryland	912
24	Texas	909
25	Tennessee	908
26	Alabama	907
27	Delaware	895
28	Missouri	891
29	Minnesota	878
30	Montana	843
31	Kansas	840
32	Wisconsin	838
33	Nebraska	797
34	Colorado	775
35	Idaho	736
36	Ohio	705
37	West Virginia	653
38	Wyoming	644
39	Kentucky	613
40	North Carolina	604
41	California	588
42	Oklahoma	566
43	Virginia	551
44	New Hampshire	482
45	Puerto Rico	435
46	Washington	422
47	Utah	366
48	Oregon	327
49	Alaska	254
50	Maine	225
51	Hawaii	197
52	Vermont	179

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arkansas	16
2	Rhode Island	16
3	Indiana	12
4	Iowa	12
5	Kansas	12
6	Missouri	12
7	Wyoming	12
8	Connecticut	11
9	Louisiana	11
10	Mississippi	11
11	Montana	11
12	South Dakota	11
13	Tennessee	11
14	Illinois	9
15	Pennsylvania	9
16	West Virginia	9
17	Wisconsin	9
18	Arizona	8
19	Michigan	8
20	Nebraska	8
21	Nevada	8
22	Kentucky	7
23	Massachusetts	7
24	New Mexico	7
25	District of Columbia	6
26	Idaho	6
27	Minnesota	6
28	New York	6
29	Ohio	6
30	Texas	6
31	California	5
32	Maryland	5
33	New Jersey	5
34	Puerto Rico	5
35	South Carolina	5
36	Alabama	4
37	Alaska	4
38	Delaware	4
39	Florida	4
40	Oklahoma	4
41	Colorado	3
42	North Carolina	3
43	North Dakota	3
44	Oregon	3
45	Georgia	2
46	Maine	2
47	Utah	2
48	Vermont	2
49	Virginia	2
50	New Hampshire	1
51	Washington	1
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	260,353	1	99
Norton	Kansas	214,699	2	99
Lincoln	Arkansas	211,072	3	99
Bon Homme	South Dakota	209,970	4	99
Dewey	South Dakota	209,437	5	99
Davidson	Tennessee	91,511	359	88
Richland	South Carolina	58,974	1544	50
York	South Carolina	48,448	2086	33
Orange	California	41,809	2381	24
Pierce	Washington	27,910	2812	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Richland	South Carolina	774	1735	44
Davidson	Tennessee	756	1773	43
York	South Carolina	605	2065	34
Orange	California	560	2148	31
Pierce	Washington	340	2576	18

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.